Riyasharma-in

#adjust_boxes(converted_boxes)
num=0
for i in adjust_boxes(converted_boxes):
        image = cv2.rectangle(img, (int(i[0]), int(i[1])), (int(i[2]), int(i[3])), (255,0,255), 1)
        cv2.putText(img, "{}".format(num+1), (int(i[0]+30), int(i[1])+30), cv2.FONT_HERSHEY_SIMPLEX , .7, 
                  (0, 0, 255), 2);
        num+=1
cv2.imwrite('result.jpg',img)

Riyasharma-in

def adjust_boxes(boxes):
    for i in range(len(boxes)):
        for j in range(i+1, len(boxes)):
            if j >= len(boxes):
                break
            # Check if the boxes intersect
            if intersect(boxes[i], boxes[j]):
                
                # Determine the overlap distance
                dx = min(boxes[i][2], boxes[j][2]) - max(boxes[i][0], boxes[j][0])

Riyasharma-in

converted_boxes = []
for box in df.values.tolist():
    x, y, w, h, x2 = box
    y2 = y + h
    converted_boxes.append([x, y, x2, y2])

Riyasharma-in

# re-order the bounding boxes by their position
for i in range(len(bboxes)-1):
    for ind in range(len(df)-1):
        if df.iloc[ind][4] > df.iloc[ind+1][0] and df.iloc[ind][1] > df.iloc[ind+1][1]:
            #print(df.iloc[ind][4] , df.iloc[ind+1][0] , df.iloc[ind][1] , df.iloc[ind+1][1])
            df.iloc[ind], df.iloc[ind+1] = df.iloc[ind+1].copy(), df.iloc[ind].copy()
            #print(df.iloc[ind], df.iloc[ind+1],'')

Riyasharma-in

# sort the bounding boxes by their x-coordinate, y-coordinate, and x2-coordinate
df = df.sort_values(["x","y", "x2"])

Riyasharma-in

# convert bounding boxes to [x, y, w, h] format
bboxes = [[box[0], box[1], box[2]-box[0], box[3]-box[1]] for box in bboxes]

# create a pandas dataframe from the bounding boxes
df = pd.DataFrame(bboxes, columns=['x','y','w', 'h'], dtype=int)

# add a column for the x-coordinate on the right side of the bounding box
df["x2"] = df["x"]+df["w"]

Riyasharma-in

import pandas as pd
import cv2

img = cv2.imread("test.jpg")
# generate some bounding boxes (x1, y1, x2, y2)
bboxes = [[0.0, 11.934242248535156, 1772.6680908203125, 122.33704376220703],
[20.308576583862305, 10.841922760009766, 738.1071166992188, 125.6768798828125],
[27.024972915649414, 141.59010314941406, 672.558349609375, 497.9985656738281],
[650.5659790039062, 152.5384979248047, 890.4132690429688, 487.71356201171875],
[890.1557006835938, 135.5287322998047, 1202.565185546875, 374.8603820800781],